Method, system, equipment and medium for calculating effectiveness of new energy capacity of considering system

Abstract

The invention relates to the field of new energy planning and design, in particular to a method, a system, equipment and a medium for calculating effectiveness of new energy capacity of a system. The method comprises the following steps: taking a supply and demand scene established by classifying supply and demand conditions of a power system as an initial clustering center, and establishing a historical scene library of a whole scene through a scene clustering algorithm; marking a scene with insufficient regional content in a historical scene library, correspondingly establishing a thermal power capacity characteristic in the scene with insufficient regional content, and fitting a wind power output characteristic and a photovoltaic output characteristic; performing system load loss probability calculation by combining the system load increase expected value, the thermal power installed capacity, the wind power installed capacity and the photovoltaic installed capacity of the target year to obtain an initial system load loss probability of the target year; respectively adjusting the wind power installed capacity and the photovoltaic installed capacity, and calculating the system load loss probability again until a preset system load loss probability requirement is met; and according to the system load and the installed capacity of the new energy, obtaining the effectiveness of the target year considering the new energy capacity of the system.

Description

Technical Field

[0001] The present invention relates to the field of new energy planning and design, and in particular to a method, system, device and medium for calculating the effectiveness of new energy capacity of a system. Background Art

[0002] As the installed capacity of new energy continues to increase, on the one hand, affected by the clean electrification process in industries such as transportation and construction, the demand for electricity load is increasing year by year; on the other hand, the scale of thermal power installed capacity is limited, and the peak load supply requires new energy to play a more important role. However, it is well known that the output of new energy is greatly affected by the weather, and its capacity reliability is low, but the highly reliable thermal power cannot meet the capacity demand of the system during peak hours. The effective capacity of new energy must be accurately calculated to guide the system installation planning or capacity market procurement, so as to ensure the reliability of the system power supply in an economical way.

[0003] At present, the calculation of the effectiveness of new energy capacity mostly adopts the direct statistical method of historical data, which does not fully consider the uncertain characteristics of new energy output. Although some studies in the existing technology have considered system reliability indicators, they have included many small-probability events, which have limited guiding significance for actual production. At the same time, when confirming the effectiveness, the effective capacity of new energy is calculated only by relying on the time synchronization characteristics of historical wind and solar output and peak load, which cannot comprehensively evaluate the operation effect of the power capacity market, resulting in a gap between the healthy and stable construction and development of the power system under a high proportion of new energy and actual needs, and cannot guarantee the long-term abundance of system capacity, affecting the reliability and stability of the power system operation; it cannot meet the overall requirements of the system, and when considering the design, operation, optimization or analysis of the power system, it is necessary to comprehensively and comprehensively consider all aspects and factors of the entire power system. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method, system, device and medium for calculating the effectiveness of the new energy capacity of the accounting system, which meets the needs of the accounting system, can directly characterize the system reliability, achieve full-scenario coverage, have a high degree of effectiveness, and ensure the power supply reliability of the system.

[0005] The present invention is achieved through the following technical solutions: The present invention provides a method for calculating the effectiveness of system new energy capacity, comprising: Constructing date feature labels of the power system based on historical weather and operation data; establishing supply and demand scenarios based on the supply and demand conditions of the power system; establishing a scenario clustering algorithm input label set based on the date feature labels, using the supply and demand scenarios as initial clustering centers, and establishing a historical scenario library of all scenarios through the scenario clustering algorithm; Based on the inter-regional purchased power data, the insufficient capacity scenarios in the region are marked in the historical scenario library, and the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenarios in the region are screened out; and the thermal power capacity characteristics under the insufficient capacity scenarios in the region are correspondingly established, and the wind power output characteristics and photovoltaic output characteristics are fitted; According to the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, the system load loss probability is calculated in combination with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, and the initial system load loss probability in the target year is obtained; Based on the initial system load loss probability, the wind power and photovoltaic installed capacities are adjusted respectively and the system load loss probability is calculated again until the preset system load loss probability requirement is met; according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability, the effectiveness of the system new energy capacity in the target year is obtained.

[0006] Optionally, a historical scenario library of the entire scenario is established through a scenario clustering algorithm, wherein the input label set of the scenario clustering algorithm is established by date feature labels of the power system constructed based on historical weather and operation data; the date feature labels of the power system include peak-to-valley difference rate of the centralized dispatching load, peak-to-valley difference rate of the system net load, average load rate of wind power / photovoltaic power, maximum starting capacity rate of thermal power units, maintenance capacity rate of thermal power units, non-shutdown capacity rate of thermal power units and system capacity shortage rate indicators.

[0007] Optionally, the supply and demand scenarios established based on the classification of supply and demand conditions of the power system specifically include four scenarios divided according to the four seasons of spring, summer, autumn and winter, each of which includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario; the two scenarios corresponding to summer and winter also include ultra-extreme scenarios.

[0008] Optionally, based on the inter-regional purchased power data, the insufficient capacity scenarios in the region are marked in the historical scenario library. Specifically, scenarios in which the system capacity shortage rate is not less than a set probability are selected through the historical scenario case library to constitute the insufficient capacity scenarios within the province.

[0009] Optionally, the thermal power capacity characteristics under the scenario of insufficient capacity in the region are established, and the wind power output characteristics and the photovoltaic output characteristics are fitted. Specifically, the thermal power capacity characteristics under the scenario of insufficient capacity in the region are established based on the Monte Carlo sampling method, the wind power output characteristics are fitted based on the Weibull distribution, and the photovoltaic output characteristics are fitted based on the kernel density estimation.

[0010] Optionally, the system load loss probability is calculated based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the capacity shortage scenario in the region, combined with the expected value of system load growth in the future target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; specifically including: Substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the insufficient capacity scenario in the region to obtain the load data of each scenario in the insufficient capacity scenario in the region; By convolving the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, the probability of insufficient capacity supply in each scenario is obtained based on the convolution calculation results; The probability of insufficient capacity supply in each scenario is combined with the scenario probability to calculate the initial system load loss probability LOLP0 in the target year.

[0011] Optionally, based on the initial system load loss probability, the wind power and photovoltaic installed capacities are adjusted respectively to recalculate the system load loss probability until the preset system load loss probability requirement is met, and the effectiveness of the system new energy capacity in the target year is obtained according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability; specifically, Keep the system load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another renewable energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The corresponding new energy capacity effectiveness is obtained according to the following formula;

[0012] In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

[0013] The present invention also provides a system for calculating the effectiveness of system new energy capacity, comprising: A historical scenario library construction module is used to construct a date feature label of the power system based on historical weather and operation data; to classify the supply and demand situation of the power system and establish a supply and demand scenario; to establish a scenario clustering algorithm input label set with the date feature label, to use the supply and demand scenario as the initial clustering center, and to establish a historical scenario library of all scenarios through the scenario clustering algorithm; A scenario screening module is used to mark the insufficient capacity scenario in the region in the historical scenario library based on the inter-regional purchased power data, and screen out the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenario in the region; and establish the thermal power capacity characteristics under the insufficient capacity scenario in the region, and fit the wind power output characteristics and photovoltaic output characteristics; The system load loss probability calculation module is used to calculate the system load loss probability based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; The new energy capacity effectiveness calculation module is used to adjust the wind power and photovoltaic installed capacities respectively based on the initial system load loss probability and recalculate the system load loss probability until the preset system load loss probability requirement is met; and obtain the effectiveness of the system new energy capacity in the target year according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability.

[0014] The historical scenario library construction module also includes a label construction unit, which is used to construct an input label set for the scenario clustering algorithm, and the input label set is established by the power system date feature labels constructed according to historical weather and operation data; the power system date feature labels include the peak-to-valley difference rate of the unified load, the peak-to-valley difference rate of the system net load, the average load rate of wind power / photovoltaic power, the maximum start-up capacity rate of the thermal power unit, the maintenance capacity rate of the thermal power unit, the non-shutdown capacity rate of the thermal power unit and the system capacity shortage rate indicators.

[0015] Optionally, the historical scenario library construction module also includes a scenario construction unit for supply and demand scenarios established based on the classification of power system supply and demand conditions, which is used to divide scenarios into four categories according to the four seasons of spring, summer, autumn and winter, each of which includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario; the two scenarios corresponding to summer and winter also include ultra-extreme scenarios.

[0016] Optionally, the system load loss probability calculation module specifically includes: The scenario load data calculation unit is used to substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the capacity shortage scenario in the region to obtain the load data of each scenario in the capacity shortage scenario in the region; A scenario capacity supply shortage probability calculation unit is used to perform convolution calculation on the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, and obtain the probability of capacity supply shortage in each scenario according to the convolution calculation result; The initial reliability index unit is used to calculate the initial target year system load loss probability LOLP0 by combining the capacity supply shortage probability of each scenario with the scenario probability, as the initial system load loss probability of the target year.

[0017] Optional, new energy capacity effectiveness calculation module, specifically including, The final target year system load loss probability unit is used to keep the load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another new energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The effectiveness calculation unit is used to obtain the effectiveness of the corresponding new energy capacity according to the following formula;

[0018] In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

[0019] The present invention also provides a computer device, comprising: Memory for storing computer programs; A processor is used to implement the calculation method of the effectiveness of the system's new energy capacity taking into account as described in the present invention when executing the computer program.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for calculating the effectiveness of the system's new energy capacity taking into account the present invention is implemented.

[0021] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method for calculating the effectiveness of the system's new energy capacity in consideration of the present invention.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: The method for calculating the effectiveness of the system's new energy capacity taken into account in the present invention can take into account the impact of various uncertain factors on the system reliability in all scenarios by classifying the supply and demand conditions of the power system in all scenarios and performing clustering processing; and can screen out the required data for the complete regional capacity shortage scenario, and use convolution calculation and the reliability requirements of the corresponding system load loss probability to adjust the capacity, thereby achieving accurate calculation of the effectiveness of the new energy capacity, meeting the reliability requirements, and comprehensively evaluating the operation of the system; guiding the healthy and stable construction and development of the power system under a high proportion of new energy, and ensuring the long-term sufficiency of the system capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the method for calculating the effectiveness of new energy capacity described in an example of the present invention.

[0024] Figure 2 It is a structural block diagram of the new energy capacity effectiveness calculation system described in an example of the present invention.

[0025] Figure 3 It is a flowchart of the practical application of the new energy capacity effectiveness calculation method described in the example of the present invention.

[0026] Figure 4 It is a structural schematic diagram of a computer device described in an example of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0029] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0030] In the present invention, "module", "device", "system" and the like refer to related entities applied to computers, such as hardware, a combination of hardware and software, software or software in execution, etc. In detail, for example, an element can be, but is not limited to, a process, a processor, an object, an executable element, an execution thread, a program and / or a computer running on a processor. In addition, an application or script program running on a server, a server can all be an element. One or more elements can be in an execution process and / or thread, and an element can be localized on a computer and / or distributed between two or more computers, and can be operated by various computer-readable media. An element can also communicate through local and / or remote processes according to a signal with one or more data packets, for example, a signal from a data that interacts with another element in a local system, a distributed system, and / or a network on the Internet through a signal to interact with other systems.

[0031] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include" and "comprise" include not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such processes, methods, articles or equipment. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or equipment that includes the elements.

[0032] In order to solve the problems that the calculation method for calculating the effective capacity of new energy only relying on the time synchronization characteristics of historical wind and solar power output and peak load is simple, the system reliability index is not directly represented, the scenario coverage is insufficient, and some studies include a large number of small-probability events in the full scenario convolution affecting the accuracy of the calculation results, the present invention proposes a method, system, equipment and medium for calculating the effectiveness of new energy capacity of the system, takes into account multiple uncertain factors and system reliability requirements, and constructs a method for calculating the effectiveness of new energy capacity.

[0033] The present invention takes into account the calculation method of the effectiveness of the new energy capacity of the system, specifically as follows Figure 1 As shown, including, S1. The supply and demand scenarios established by classifying the supply and demand conditions of the power system are used as the initial clustering center, and a historical scenario library of all scenarios is established through a scenario clustering algorithm; Among them, the clustering algorithm in the present invention uses the labeled data in the historical operation data to establish the initial clustering center. The labeled data is actually the supply and demand scenario in the power system. The existing fuzzy clustering method randomly generates the initial clustering center and then performs clustering calculations. It is easy to fall into the trap of selecting two centers with high similarity, resulting in the randomness of the clustering results being too strong. Therefore, the setting of the clustering center of the present invention can not only improve the efficiency of clustering, but also achieve full scene coverage and avoid the loss of some scenes. Finally, according to the clustering results, a historical scene library of the whole scene is established.

[0034] S2. Based on the inter-regional purchased power data, the insufficient capacity scenario in the region is marked in the historical scenario library, and the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenario in the region are screened out; and the thermal power capacity characteristics under the insufficient capacity scenario in the region are correspondingly established, and the wind power output characteristics and photovoltaic output characteristics are fitted; The regions can be divided by province or region. In this embodiment, provinces are used as examples. For the sending province, the capacity supply is usually sufficient, but due to the high proportion of new energy and frequent extreme weather, there is occasionally insufficient capacity in the province. Therefore, it is of great practical significance to analyze the capacity adequacy and system reliability based on this scenario.

[0035] By calculating the corresponding data memory selected, the required data can be obtained.

[0036] S3. Based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, the system load loss probability is calculated to obtain the initial system load loss probability in the target year; Among them, the system load loss probability can be used as an indicator of system reliability to measure and judge the system reliability, which is actually to obtain the current reliability based on future needs.

[0037] S4. Based on the initial system load loss probability, the wind power and photovoltaic installed capacities are adjusted respectively and the system load loss probability is recalculated until the preset system load loss probability requirement is met; and the effectiveness of the system new energy capacity in the target year is obtained according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability.

[0038] Among them, on the basis of the current reliability in the result of step S3, that is, on the basis of the current system load loss probability, the installed capacity of new energy is adjusted by a single variable, and the system load loss probability that meets the system reliability is obtained through continuous iteration. According to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability, the effectiveness of photovoltaic and wind power in new energy is calculated, so that the effectiveness of wind power capacity and photovoltaic capacity can be obtained.

[0039] In one example of the present invention, the input label set of the scene clustering algorithm is established by the date feature label of the power system constructed according to the historical weather and operation data; by accessing or importing the historical operation data of the power system, including the historical weather information and the corresponding operation data, and then statistically analyzing the peak-to-valley difference rate of the centralized load, the peak-to-valley difference rate of the system net load, the average load rate of wind power / photovoltaic power, the maximum startup capacity rate of the thermal power unit, the maintenance capacity rate of the thermal power unit, the non-stop capacity rate of the thermal power unit and the system capacity shortage rate as the date feature label of the power system. And the scene input label set is established with the calculation results of the date features corresponding to the above date feature labels.

[0040] In one example of the present invention, the supply and demand scenarios established based on the classification of the supply and demand conditions of the power system specifically include four categories of scenarios divided according to the four seasons of spring, summer, autumn and winter, each of which includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario; the two categories of scenarios corresponding to summer and winter also include ultra-extreme scenarios; there are a total of 14 scenarios for four seasons, which are used as the initial clustering centers.

[0041] In one embodiment of the present invention, based on the inter-regional purchased power data, the insufficient capacity scenarios in the region are marked in the historical scenario library. Specifically, scenarios in which the system capacity shortage rate is not less than the set probability are selected through the historical scenario case library to constitute the insufficient capacity scenarios within the province.

[0042] The set probability is based on the system capacity. In this preferred example, the scenario with a probability of not less than 0.95 is selected to screen the scenario with insufficient capacity, and the corresponding number of days is calculated. The output data of thermal power, wind power and photovoltaic power are also screened, corresponding to the maximum declared capacity value of thermal power, wind power output value and photovoltaic output value.

[0043] In one embodiment of the present invention, the thermal power capacity characteristics under the scenario of insufficient capacity in the region are established, and the wind power output characteristics and photovoltaic output characteristics are fitted. Specifically, the thermal power capacity characteristics under the scenario of insufficient capacity in the region are established based on the Monte Carlo sampling method, the wind power output characteristics are fitted based on the Weibull distribution, and the photovoltaic output characteristics are fitted based on the kernel density estimation. Since the maximum declared capacity of thermal power reflects the power generation capacity of the unit and does not include maintenance and non-stop information, it is necessary to construct the available capacity value of the thermal power unit when analyzing the thermal power characteristics. Therefore, it is necessary to use the Monte Carlo sampling method to establish the thermal power capacity characteristics under the scenario of insufficient capacity in the region. The output characteristics of the wind turbine unit have the characteristics of Weibull distribution of load, so the Weibull function is used to fit the probability characteristics of wind power processing on a monthly basis. Since the output of the photovoltaic unit is greatly affected by the weather and has no output at night, the traditional Weibull distribution is not effective, so the kernel density estimation method is used for fitting.

[0044] In one embodiment of the present invention, the system load loss probability is calculated based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics in the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the future target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; specifically, Substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the insufficient capacity scenario in the region to obtain the load data of each scenario in the insufficient capacity scenario in the region; By convolving the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, the probability of insufficient capacity supply in each scenario is obtained based on the convolution calculation results; The probability of insufficient capacity supply in each scenario is combined with the scenario probability to calculate the target year system load loss probability LOLP0. The system load loss probability can be used as a system reliability indicator to judge the system reliability.

[0045] In the examples described in the present invention, it can be seen that when the scene is actually constructed, full scene coverage is adopted. However, when performing convolution calculations for reliability, it is actually performed under the constraint of insufficient capacity in the area, which is equivalent to selecting the convolution of the scene, so that it can be more accurate and efficient.

[0046] In one embodiment of the present invention, based on the initial system load loss probability, the wind power and photovoltaic installed capacities are respectively adjusted to recalculate the system load loss probability until the preset system load loss probability requirement is met, and the effectiveness of the system new energy capacity in the target year is obtained according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability; specifically, Keep the system load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another renewable energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The corresponding new energy capacity effectiveness is obtained according to the following formula;

[0047] In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

[0048] Among them, for wind power and photovoltaic power, the corresponding installed capacity change values ​​on the denominator will have corresponding changes. The calculation methods of the two are similar. Under the premise of meeting the preset system load loss probability LOLP1 requirements, the effectiveness of the new energy capacity in the target year is calculated based on the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability.

[0049] Corresponding to the above method, the present invention also provides a system for calculating the effectiveness of the system's new energy capacity, such as Figure 2 As shown, it includes, The historical scenario library construction module 201 uses the supply and demand scenarios established by classifying the supply and demand conditions of the power system as the initial clustering center, and establishes a historical scenario library of all scenarios through a scenario clustering algorithm; The scenario screening module 202 is used to mark the insufficient capacity scenario in the region in the historical scenario library based on the inter-regional purchased power data, and screen out the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenario in the region; and establish the thermal power capacity characteristics under the insufficient capacity scenario in the region, and fit the wind power output characteristics and photovoltaic output characteristics; The system load loss probability calculation module 203 is used to calculate the system load loss probability according to the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics in the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; The new energy capacity effectiveness calculation module 204 is used to adjust the wind power and photovoltaic installed capacities respectively based on the initial system load loss probability and recalculate the system load loss probability until the preset system load loss probability requirement is met; and obtain the effectiveness of the system new energy capacity in the target year according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability.

[0050] In a preferred example of the system described in the present invention, the historical scenario library construction module also includes a label construction unit for constructing an input label set for a scenario clustering algorithm, wherein the input label set is established by date feature labels of the power system constructed based on historical weather and operation data; the date feature labels of the power system include indicators such as the peak-to-valley difference rate of the coordinated load, the peak-to-valley difference rate of the system net load, the average load rate of wind power / photovoltaic power, the maximum start-up capacity rate of thermal power units, the maintenance capacity rate of thermal power units, the non-shutdown capacity rate of thermal power units and the system capacity shortage rate.

[0051] In a preferred example of the system described in the present invention, the historical scenario library construction module also includes a scenario construction unit for supply and demand scenarios established based on the classification of supply and demand conditions of the power system, which is used to divide scenarios into four categories according to the four seasons of spring, summer, autumn and winter, each of which includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario; the two scenarios corresponding to summer and winter also include ultra-extreme scenarios.

[0052] In a preferred embodiment of the system of the present invention, the system load loss probability calculation module specifically includes: The scenario load data calculation unit is used to substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the capacity shortage scenario in the region to obtain the load data of each scenario in the capacity shortage scenario in the region; A scenario capacity supply shortage probability calculation unit is used to perform convolution calculation on the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, and obtain the probability of capacity supply shortage in each scenario according to the convolution calculation result; The initial reliability index unit is used to calculate the initial target year system load loss probability LOLP0 by combining the capacity supply shortage probability of each scenario with the scenario probability, as the initial system load loss probability of the target year.

[0053] In a preferred embodiment of the system of the present invention, the new energy capacity effectiveness calculation module specifically includes: The final target year system load loss probability unit is used to keep the load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another new energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The effectiveness calculation unit is used to obtain the effectiveness of the corresponding new energy capacity according to the following formula;

[0054] In the formula, △P loadis the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

[0055] In actual application scenarios, such as Figure 3 Under the framework shown, taking into account system reliability and scenario characteristics, the steps of the new energy capacity effectiveness calculation method are as follows.

[0056] 1. Establish a historical scenario library based on historical weather conditions and power system supply and demand conditions combined with scenario clustering algorithm; (1) Construct date feature labels based on historical data, which includes historical weather data and historical operation data. Figure 3 The historical database in .

[0057] By accessing or importing the system's historical operation data, the following indicators are counted as daily characteristic labels of the power system: 1) Peak-to-valley difference rate of unified load regulation L q ; The peak-to-valley difference rate of the unified load is the ratio of the difference between the maximum and minimum load values ​​of the day to the maximum load value. The calculation formula is:

[0058] Where: L MAX is the maximum value of daily unified load regulation, L MIN It is the minimum value of daily unified load.

[0059] 2) System net load peak-to-valley difference rate LN q ; The peak-to-valley difference rate of the system net load is the ratio between the difference between the maximum and minimum net load values ​​of the day and the maximum net load value. The calculation formula is:

[0060]

[0061] Where: LN MAX is the maximum daily net load, LN MIN is the minimum value of daily net load; LN is the net load, L To regulate the system load, LS To deliver power to the system, L wind To provide wind power for the system, L solar Provide wind power for the system.

[0062] 3) Average load rate of wind power / photovoltaic power ; The average wind power / photovoltaic load rate is the ratio of the average wind power / photovoltaic output to its installed capacity on that day, and the calculation formula is:

[0063]

[0064] Where: Q wind_set is the installed capacity of wind power in the system, Q solar_set The installed capacity of wind power in the system.

[0065] 4) Maximum operating capacity rate of thermal power units ; The maximum operating capacity rate of thermal power units is the ratio of the maximum operating capacity of thermal power units on that day to their installed capacity. The calculation formula is:

[0066] Where: Q fire_open is the maximum operating capacity of the system thermal power on that day, Q fire_set It is the thermal power installed capacity of the system.

[0067] 5) Maintenance capacity rate of thermal power units ; The maintenance capacity rate of thermal power units is the ratio of the thermal power maintenance capacity to its installed capacity on that day, and the calculation formula is:

[0068] Where: Q fire_overhaul is the maximum maintenance capacity of the system thermal power on that day, Q fire_set It is the thermal power installed capacity of the system.

[0069] 6) Non-stop capacity rate of thermal power units ; The non-stop capacity rate of thermal power units is the ratio of the non-stop capacity of thermal power units on that day to their installed capacity. The calculation formula is:

[0070] Where: Q none_stop Q is the non-stop capacity of the system’s thermal power on that day, fire_set It is the thermal power installed capacity of the system.

[0071] 7) System capacity shortage rate ; The system capacity shortage rate is the ratio of the number of periods of insufficient capacity supply in the province on the same day. The calculation formula is:

[0072]

[0073] Where: N loss is the total number of load loss periods, Q fire_set is the installed capacity of thermal power in the system, Q fire_overhaul is the maximum maintenance capacity of the system thermal power on that day, Q none_stop The non-stop capacity of the system’s thermal power on that day. L To regulate the system load, LS To deliver power to the system, L wind To provide wind power for the system, L solar Provide wind power for the system.

[0074] (2) Scene clustering calculation.

[0075] 1) Build a scene label set.

[0076] The scene input label set is established based on the date feature calculation results. The input matrix is X=[L q ,LN q ,L wind,q ,L solar,q ,q fire_open ,q fire_overhaul ,q none_stop ,q loss ,D] Where: D is the date, which does not participate in the clustering calculation. As the index label of input X, it can classify the corresponding scene according to the date.

[0077] 2) Scene clustering calculation based on fuzzy clustering of directional features.

[0078] a. Define the criterion function. You can choose a variety of clustering functions for calculation.

[0079] b. Select the initial cluster centers.

[0080] The traditional fuzzy clustering method randomly generates the initial cluster center and then performs cluster calculation. It is easy to fall into the trap of selecting two centers with high similarity, which leads to the randomness of the clustering results being too strong. Therefore, according to the supply and demand of the power system, the initial cluster center is established based on the scene data marked in the historical operation data, including the following categories:

[0081] c. Then divide the sample into the class according to the distance from the sample to the cluster center; d. Finally, recalculate the cluster center of each class; e. The above process is repeated until the criterion function reaches a minimum and the clustering result is obtained. The data sets within different cluster center ranges correspond to different scenarios.

[0082] (3) Calculate scenario probability Calculate the probability of different scenarios in each season according to quarterly statistics, taking summer as an example:

[0083] Where: The probability of loose supply and demand in summer, The probability of tight supply and demand in summer, is the probability of extreme scenarios in summer, The probability of super extreme scenarios in summer.

[0084]

[0085] Where: D loose is the total number of days with tight supply and demand in summer, D summer The total number of days in summer.

[0086] (4) Establish a historical scenario case library.

[0087] Based on the clustering results in step (2), a historical scenario case library is established, and the storage format is as follows:

[0088] 2. Mark the scenarios of insufficient power within the province based on inter-provincial purchased electricity data.

[0089] For the sending provinces, capacity supply is usually sufficient, but due to the high proportion of renewable energy and frequent extreme weather, there is occasionally insufficient capacity within the province. Therefore, it is of great practical significance to analyze capacity adequacy and system reliability based on this scenario.

[0090] Scenarios with a system capacity shortage rate ≥ 0.95 are selected through the historical scenario case library to form provincial capacity shortage scenarios.

[0091] Calculate the probability of insufficient storage capacity, that is: P 省内容量不足场景概率 = Number of days with insufficient province capacity / number of days with historical statistics.

[0092] 3. Statistical analysis of thermal power, wind power and photovoltaic power output data in the scenario of insufficient provincial capacity Based on the date labels of the scenarios with insufficient provincial capacity, the maximum declared capacity of thermal power, wind power output and photovoltaic output are filtered out.

[0093] 4. Establish the thermal power capacity characteristics under the capacity shortage scenario based on the Monte Carlo sampling method, fit the wind power output characteristics based on the Weibull distribution, and fit the photovoltaic output characteristics based on the kernel density estimation. Complete the resource characteristic fitting calculation through steps 2, 3 and 4.

[0094] (1) Thermal power capacity characteristics.

[0095] Since the maximum declared capacity of thermal power reflects the power generation capacity of the unit and does not include maintenance and non-shutdown information, it is necessary to construct the available capacity value of the thermal power unit when analyzing the characteristics of thermal power.

[0096] 1) Assuming that the total number of units is 100 and 10 units are planned to be overhauled, 90 [0-1] random numbers are generated; 2) Determine whether the unit is in a non-stop state, that is, when the random number corresponding to the unit is greater than 0.98, the unit status is in a non-stop state; 3) Calculate the total available capacity of thermal power units If the unit is in non-stop state, its capacity is 0; if the unit is in normal state, its capacity is the corresponding maximum declared capacity. The specific calculation formula is:

[0097] Where: S i is the state of unit i, P cap_bid,i Declare the maximum capacity for unit i.

[0098] 4) Repeat steps 1)-3) 1,000,000 times to obtain the final sampling result and perform probability statistics.

[0099] (2) Wind turbine output characteristics.

[0100] The output characteristic load of wind turbines is characterized by Weibull distribution, so the Weibull function is used to fit the probability characteristics of wind power processing on a monthly basis.

[0101] Here, x is the value of the random variable, λ is the scale parameter, and k is the shape parameter. The values ​​of λ and k determine the shape and scale of the distribution.

[0102] (3) Output characteristics of photovoltaic units.

[0103] Since the output of photovoltaic units is greatly affected by the weather and has a large fluctuation, and there is no output at night, the traditional Weibull distribution is not effective, so the kernel density estimation method is used for fitting.

[0104] 5. Calculate the probability of system load loss based on the expected system load growth in the future target year, the transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity to obtain the initial system load loss probability; complete the initial system reliability calculation.

[0105] (1) Substitute the expected value of system load growth in the target year, the value of external transmission growth, thermal power installed capacity, wind power installed capacity, and photovoltaic installed capacity into the provincial capacity shortage scenario obtained based on historical operation data in step 2 to obtain the load data of each scenario in the regional capacity shortage scenario; (2) By convolving the load data of each scenario with the thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data; (3) Calculate the convolution results to obtain the probability of insufficient capacity supply in each scenario; (4) The capacity supply shortage probability of each scenario is combined with the scenario probability to calculate the system load loss probability LOLP0 in the initial target year.

[0106] 6. Based on the result, adjust the wind power and photovoltaic installed capacity respectively and calculate again until the system load loss probability meets the preset system load loss probability requirement, and finally obtain the calculation result of the effectiveness of the target year's new energy capacity. The process of meeting the preset system load loss probability requirement is the equivalent load calculation, and the wind and solar capacity effectiveness calculation is completed on this basis.

[0107] (1) Calculation of wind power capacity effectiveness 1) Keeping the system load and photovoltaic data unchanged, increase the wind power installed capacity by 1MW and perform the convolution calculation in step 5 until the system load loss probability requirement meets the preset system load loss probability requirement LOLP1; 2) Calculate the effectiveness of wind power capacity

[0108] In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P wind It is the change value of wind power installed capacity before and after adjustment.

[0109] (2) The calculation method of photovoltaic capacity effectiveness is similar to that of wind power. During the process, the installed capacity of wind power is kept unchanged and the installed capacity of photovoltaic power is added to calculate the capacity effectiveness.

[0110] In one embodiment of the present invention, a computer device 400 is also provided, such as Figure 4 As shown, including: Memory 401, used for storing computer programs; The processor 402 is configured to implement the method for calculating the effectiveness of the system's new energy capacity taking into account as described in any one of the examples of the present invention when executing the computer program.

[0111] The system may further include a computer program 403 stored in the memory 401 and executable on the at least one processor 402 and at least one communication bus 404 .

[0112] The memory 401 can be used to store the computer program 403. The processor 402 implements the steps of any one of the methods for calculating the effectiveness of the system's new energy capacity by running or executing the computer program stored in the memory 401 and calling the data stored in the memory 401. The memory 401 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the computer device 400, etc. In addition, the memory 401 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0113] The at least one processor 402 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 402 may be a microprocessor or any conventional processor, etc. The processor 402 is the control center of the computer device 400, and uses various interfaces and lines to connect various parts of the entire computer device 400.

[0114] The memory 401 in the computer device 400 stores a plurality of instructions to implement a method for calculating the effectiveness of system new energy capacity. The processor 402 can execute the plurality of instructions to implement: If the module / unit integrated in the computer device 400 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0115] In one example of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the method for calculating the effectiveness of the system's new energy capacity taking into account is implemented as described in any one of the examples of the present invention.

[0116] In one example of the present invention, a computer program product is also provided, comprising a computer program, which, when executed by a processor, implements the steps of the method for calculating the effectiveness of the system's new energy capacity taking into account as described in any one of the examples of the present invention.

[0117] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the effectiveness of the system's new energy capacity, characterized in that: include, The supply and demand scenarios established by classifying the supply and demand conditions of the power system are used as the initial clustering centers, and a historical scenario library of all scenarios is established through a scenario clustering algorithm; Based on the inter-regional purchased power data, the insufficient capacity scenarios in the region are marked in the historical scenario library, and the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenarios in the region are screened out; and the thermal power capacity characteristics under the insufficient capacity scenarios in the region are correspondingly established, and the wind power output characteristics and photovoltaic output characteristics are fitted; According to the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, the system load loss probability is calculated in combination with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, and the initial system load loss probability in the target year is obtained; Based on the initial system load loss probability, the wind power and photovoltaic installed capacities are adjusted respectively and the system load loss probability is calculated again until the preset system load loss probability requirement is met; according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability, the effectiveness of the system new energy capacity in the target year is obtained.

2. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: The historical scenario library of the whole scenario is established through the scenario clustering algorithm, wherein the input label set of the scenario clustering algorithm is established by the date feature labels of the power system constructed according to the historical weather and operation data; the date feature labels of the power system include the peak-to-valley difference rate of the centralized load, the peak-to-valley difference rate of the system net load, the average load rate of wind power / photovoltaic power, the maximum start-up capacity rate of the thermal power unit, the maintenance capacity rate of the thermal power unit, the non-shutdown capacity rate of the thermal power unit and the system capacity shortage rate indicators.

3. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: The supply and demand scenarios established based on the classification of supply and demand conditions of the power system specifically include four scenarios divided according to the four seasons of spring, summer, autumn and winter. Each scenario includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario. The two scenarios corresponding to summer and winter also include super-extreme scenarios.

4. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: Based on the inter-regional purchased power data, the capacity shortage scenarios in the region are marked in the historical scenario library. Specifically, scenarios in which the system capacity shortage rate is not less than the set probability are selected through the historical scenario case library to constitute the capacity shortage scenarios within the province.

5. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: The thermal power capacity characteristics under the scenario of insufficient capacity in the region are established, and the wind power output characteristics and the photovoltaic output characteristics are fitted. Specifically, the thermal power capacity characteristics under the scenario of insufficient capacity in the region are established based on the Monte Carlo sampling method, the wind power output characteristics are fitted based on the Weibull distribution, and the photovoltaic output characteristics are fitted based on the kernel density estimation.

6. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: The system load loss probability is calculated based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the future target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; specifically, Substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the insufficient capacity scenario in the region to obtain the load data of each scenario in the insufficient capacity scenario in the region; By convolving the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, the probability of insufficient capacity supply in each scenario is obtained based on the convolution calculation results; The probability of insufficient capacity supply in each scenario is combined with the scenario probability to calculate the initial system load loss probability LOLP0 in the target year.

7. The method for calculating the effectiveness of the system's new energy capacity according to claim 1, characterized in that: Based on the initial system load loss probability, the wind power and photovoltaic installed capacities are adjusted respectively to recalculate the system load loss probability until the preset system load loss probability requirement is met, and the effectiveness of the system new energy capacity in the target year is obtained according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability; Specifically include: Keep the system load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another renewable energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The corresponding new energy capacity effectiveness is obtained according to the following formula; In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

8. A system for calculating the effectiveness of the system's new energy capacity, characterized in that: include, The historical scenario library construction module uses the supply and demand scenarios established by classifying the supply and demand conditions of the power system as the initial clustering center, and establishes a historical scenario library of all scenarios through the scenario clustering algorithm; A scenario screening module is used to mark the insufficient capacity scenario in the region in the historical scenario library based on the inter-regional purchased power data, and screen out the output data of thermal power, wind power and photovoltaic power under the insufficient capacity scenario in the region; and establish the thermal power capacity characteristics under the insufficient capacity scenario in the region, and fit the wind power output characteristics and photovoltaic output characteristics; The system load loss probability calculation module is used to calculate the system load loss probability based on the thermal power capacity characteristics, wind power output characteristics and photovoltaic output characteristics under the scenario of insufficient capacity in the region, combined with the expected value of system load growth in the target year, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity, to obtain the initial system load loss probability in the target year; The new energy capacity effectiveness calculation module is used to adjust the wind power and photovoltaic installed capacities respectively based on the initial system load loss probability and recalculate the system load loss probability until the preset system load loss probability requirement is met; and obtain the effectiveness of the system new energy capacity in the target year according to the system load and new energy installed capacity corresponding to the initial system load loss probability and the preset system load loss probability.

9. The system for calculating the effectiveness of the system's new energy capacity according to claim 8, characterized in that: The historical scenario library construction module also includes a label construction unit, which is used to construct an input label set for the scenario clustering algorithm, and the input label set is established by the power system date feature labels constructed according to historical weather and operation data; the power system date feature labels include the peak-to-valley difference rate of the unified load, the peak-to-valley difference rate of the system net load, the average load rate of wind power / photovoltaic power, the maximum start-up capacity rate of the thermal power unit, the maintenance capacity rate of the thermal power unit, the non-shutdown capacity rate of the thermal power unit and the system capacity shortage rate indicators.

10. The system for calculating the effectiveness of the system's new energy capacity according to claim 8, characterized in that: The historical scenario library construction module also includes a scenario construction unit for supply and demand scenarios established based on the classification of power system supply and demand conditions, which is used to divide scenarios into four categories according to the four seasons of spring, summer, autumn and winter. Each category of scenarios includes a tight supply and demand scenario, a loose supply and demand scenario and an extreme scenario; the two categories of scenarios corresponding to summer and winter also include ultra-extreme scenarios.

11. The system for calculating the effectiveness of the system's new energy capacity according to claim 8, characterized in that: The system load loss probability calculation module specifically includes: The scenario load data calculation unit is used to substitute the expected load growth value, transmission growth value, thermal power installed capacity, wind power installed capacity and photovoltaic installed capacity of the system in the target year into the capacity shortage scenario in the region to obtain the load data of each scenario in the capacity shortage scenario in the region; A scenario capacity supply shortage probability calculation unit is used to perform convolution calculation on the load data of each scenario with the thermal power output characteristics, wind power output characteristics and photovoltaic output characteristics data, and obtain the probability of capacity supply shortage in each scenario according to the convolution calculation result; The initial reliability index unit is used to calculate the initial target year system load loss probability LOLP0 by combining the capacity supply shortage probability of each scenario with the scenario probability, as the initial system load loss probability of the target year.

12. The system for calculating the effectiveness of the system's new energy capacity according to claim 8, characterized in that: New energy capacity effectiveness calculation module, specifically including: The final target year system load loss probability unit is used to keep the load unchanged, keep one of the wind power data and the photovoltaic data unchanged, and increase the installed capacity of another new energy source with a set step size, by convolving the load data of each scenario in the insufficient capacity scenario in the region with the adjusted thermal power output characteristics, wind power output characteristics, and photovoltaic output characteristics data until the system load loss probability meets the preset system load loss probability LOLP1 requirement; The effectiveness calculation unit is used to obtain the effectiveness of the corresponding new energy capacity according to the following formula; In the formula, △P load is the difference between the mean system load corresponding to LOLP0 and the mean system load corresponding to LOLP1; △P is the change in new energy installed capacity before and after adjustment.

13. A computer device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the method for calculating the effectiveness of system new energy capacity as described in any one of claims 1 to 7 when executing the computer program.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for calculating the effectiveness of the system new energy capacity is implemented as described in any one of claims 1 to 7.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for calculating the effectiveness of system new energy capacity are implemented as described in any one of claims 1 to 7.

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